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Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is usually detected late in the disease process. Clinical workup through imaging and tissue biopsies is often complex and expensive due to a paucity of reliable biomarkers. We used an advanced multiplexed plasmonic assay to analyze circulating tumor-derived extracellular vesicles (tEVs) in more than 100 clinical populations. Using EV-based protein marker profiling, we identified a signature of five markers (PDACEV signature) for PDAC detection. In our prospective cohort, the accuracy for the PDACEV signature was 84% [95% confidence interval (CI), 69 to 93%] but only 63 to 72% for single-marker screening. One of the best markers, GPC1 alone, had a sensitivity of 82% (CI, 60 to 95%) and a specificity of 52% (CI, 30 to 74%), whereas the PDACEV signature showed a sensitivity of 86% (CI, 65 to 97%) and a specificity of 81% (CI, 58 to 95%). The PDACEV signature of tEVs offered higher sensitivity, specificity, and accuracy than the existing serum marker (CA 19-9) or single-tEV marker analyses. This approach should improve the diagnosis of pancreatic cancer.

SUBMITTER: Yang KS

PROVIDER: S-EPMC5846089 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy.

Yang Katherine S KS Im Hyungsoon H Hong Seonki S Pergolini Ilaria I Del Castillo Andres Fernandez AF Wang Rui R Clardy Susan S Huang Chen-Han CH Pille Craig C Ferrone Soldano S Yang Robert R Castro Cesar M CM Lee Hakho H Del Castillo Carlos Fernandez CF Weissleder Ralph R

Science translational medicine 20170501 391

Pancreatic ductal adenocarcinoma (PDAC) is usually detected late in the disease process. Clinical workup through imaging and tissue biopsies is often complex and expensive due to a paucity of reliable biomarkers. We used an advanced multiplexed plasmonic assay to analyze circulating tumor-derived extracellular vesicles (tEVs) in more than 100 clinical populations. Using EV-based protein marker profiling, we identified a signature of five markers (PDAC<sup>EV</sup> signature) for PDAC detection. ...[more]

PMID: 28539469

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Project description:The prognosis of pancreatic ductal adenocarcinoma (PDAC) is one of the most dismal of all cancers and the median survival of PDAC patients is only 6-8 months after diagnosis. While decades of research effort have been focused on early diagnosis and understanding of molecular mechanisms, few clinically useful markers have been universally applied. To improve the treatment and management of PDAC, it is equally relevant to identify prognostic factors for optimal therapeutic decision-making and patient survival. Compelling evidence have suggested the potential use of extracellular vesicles (EVs) as non-invasive biomarkers for PDAC. The aim of this study was thus to identify non-invasive plasma-based EV biomarkers for the prediction of PDAC patient survival after surgery. Plasma EVs were isolated from a total of 258 PDAC patients divided into three independent cohorts (discovery, training and validation). RNA sequencing was first employed to identify differentially-expressed EV mRNA candidates from the discovery cohort (n = 65) by DESeq2 tool. The candidates were tested in a training cohort (n = 91) by digital droplet polymerase chain reaction (ddPCR). Cox regression models and Kaplan-Meier analyses were used to build an EV signature which was subsequently validated on a multicenter cohort (n = 83) by ddPCR. Transcriptomic profiling of plasma EVs revealed differentially-expressed mRNAs between long-term and short-term PDAC survivors, which led to 10 of the top-ranked candidate EV mRNAs being tested on an independent training cohort with ddPCR. The results of ddPCR enabled an establishment of a novel prognostic EV mRNA signature consisting of PPP1R12A, SCN7A and SGCD for risk stratification of PDAC patients. Based on the EV mRNA signature, PDAC patients with high risk displayed reduced overall survival (OS) rates compared to those with low risk in the training cohort (p = 0.014), which was successfully validated on another independent cohort (p = 0.024). Interestingly, the combination of our signature and tumour stage yielded a superior prognostic performance (p = 0.008) over the signature (p = 0.022) or tumour stage (p = 0.016) alone. It is noteworthy that the EV mRNA signature was demonstrated to be an independent unfavourable predictor for PDAC prognosis. This study provides a novel and non-invasive prognostic EV mRNA signature for risk stratification and survival prediction of PDAC patients.

Project description:Background:Current magnetic resonance imaging (MRI) of pancreatic disease is qualitative in nature. Quantitative imaging offers several advantages, including increased reproducibility and sensitivity to detect mild or diffuse disease. The role of multiparametric mapping MRI in characterizing various tissue types in pancreatic disease such as chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) has rarely been evaluated. Purpose:To evaluate the feasibility of multiparametric mapping [T1, T2, and apparent diffusion coefficient (ADC)] in defining tissue characteristics that occur in CP and PDAC to improve disease diagnosis. Materials and Methods:Pancreatic MRI was performed in 17 patients with PDAC undergoing therapy, 7 patients with CP, and 29 healthy volunteers with no pancreatic disease. T1 modified Look-Locker Inversion Recovery (T1 MOLLI), T2-prepared gradient-echo, and multi-slice single-shot echo-planar diffusion weighted imaging (SS-EPI DWI) sequences were used for data acquisition. Regions of interest (ROIs) of pancreas in PDAC, CP, and control subjects were outlined by an experienced radiologist. One-way analysis of variance (ANOVA) was used to compare the difference between groups and regions of the pancreas, and Tukey tests were used for multiple comparison testing within groups. Receiver operator characteristic (ROC) curves were analyzed, and the areas under the curves (AUCs) were calculated using single parameter and combined parameters, respectively. Results:T1, T2, and ADC values of the entire pancreas among PDAC, CP, and control subjects; and between upstream and downstream portions of the pancreas in PDAC patients were all significantly different (p < 0.05). The AUC values were 0.90 for T1, 0.55 for T2, and 0.71 for ADC for independent prediction of PDAC. By combining T1, T2, and ADC, the AUC value was 0.94 (sensitivity 91.54%, specificity 85.81%, 95% CI: 0.92-0.96), which yielded higher accuracy than any one parameter only (p < 0.001). Conclusion:Multiparametric mapping MRI is feasible for the evaluation of the differences between PDAC, CP, and normal pancreas tissues. The combination of multiple parameters of T1, T2, and ADC provides a higher accuracy than any single parameter alone in tissue characterization of the pancreas.

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Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy.

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Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy.

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